Understanding AI Agent Protocols
Explore how A2A, MCP, and ACP empower AI agents to collaborate and communicate efficiently
🤖 Agent-to-Agent (A2A)
Facilitating AI Agent Interoperability: Developed by Google, A2A is an open protocol enabling AI agents to discover, communicate, and collaborate across diverse systems. Each agent presents an "Agent Card" — a JSON descriptor detailing identity, capabilities, endpoints, and authentication. This fosters seamless task negotiation and communication.
Sources: DataCamp, Medium
🧠Model Context Protocol (MCP)
Bridging AI Models and Data Sources: Created by Anthropic, MCP is an open standard allowing AI models to connect to various data systems like content repositories and business tools. It enhances contextual relevance by offering a universal interface to external data.
Sources: Anthropic, Humanloop
🔗 Agent Communication Protocol (ACP)
Enabling Structured AI Agent Collaboration: Proposed by BeeAI and IBM, ACP is optimized for structured, real-time communication between AI agents in shared environments. Unlike cloud-native protocols, it emphasizes low-latency, local-first coordination — ideal for performance-critical scenarios.
Sources: GoCodeo
TL;DR
A2A, MCP, and ACP are foundational protocols enabling AI agents to communicate, access data, and collaborate effectively. Mastering these is key for AI engineers building interoperable, intelligent systems.
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